Overview

Dataset statistics

Number of variables21
Number of observations42307
Missing cells1771
Missing cells (%)0.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory6.8 MiB
Average record size in memory168.0 B

Variable types

Numeric8
Categorical5
DateTime2
Text6

Alerts

LowDoc is highly imbalanced (63.8%)Imbalance
MIS_Status is highly imbalanced (50.8%)Imbalance
RevLineCr has 1079 (2.6%) missing valuesMissing
LowDoc has 531 (1.3%) missing valuesMissing
Unnamed: 0 is uniformly distributedUniform
Unnamed: 0 has unique valuesUnique
NoEmp has 2994 (7.1%) zerosZeros
CreateJob has 28889 (68.3%) zerosZeros
RetainedJob has 26056 (61.6%) zerosZeros
FranchiseCode has 26392 (62.4%) zerosZeros
Sector has 9798 (23.2%) zerosZeros

Reproduction

Analysis started2024-01-27 15:11:12.066705
Analysis finished2024-01-27 15:11:36.460615
Duration24.39 seconds
Software versionydata-profiling vv4.6.4
Download configurationconfig.json

Variables

Unnamed: 0
Real number (ℝ)

UNIFORM  UNIQUE 

Distinct42307
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21153
Minimum0
Maximum42306
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size330.6 KiB
2024-01-27T15:11:36.571858image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2115.3
Q110576.5
median21153
Q331729.5
95-th percentile40190.7
Maximum42306
Range42306
Interquartile range (IQR)21153

Descriptive statistics

Standard deviation12213.123
Coefficient of variation (CV)0.57737074
Kurtosis-1.2
Mean21153
Median Absolute Deviation (MAD)10577
Skewness0
Sum8.9491997 × 108
Variance1.4916038 × 108
MonotonicityStrictly increasing
2024-01-27T15:11:36.832291image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1
 
< 0.1%
28199 1
 
< 0.1%
28201 1
 
< 0.1%
28202 1
 
< 0.1%
28203 1
 
< 0.1%
28204 1
 
< 0.1%
28205 1
 
< 0.1%
28206 1
 
< 0.1%
28207 1
 
< 0.1%
28208 1
 
< 0.1%
Other values (42297) 42297
> 99.9%
ValueCountFrequency (%)
0 1
< 0.1%
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
ValueCountFrequency (%)
42306 1
< 0.1%
42305 1
< 0.1%
42304 1
< 0.1%
42303 1
< 0.1%
42302 1
< 0.1%
42301 1
< 0.1%
42300 1
< 0.1%
42299 1
< 0.1%
42298 1
< 0.1%
42297 1
< 0.1%

Term
Real number (ℝ)

Distinct228
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean108.60167
Minimum0
Maximum360
Zeros94
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size330.6 KiB
2024-01-27T15:11:37.068020image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile13
Q156
median82
Q3168
95-th percentile293
Maximum360
Range360
Interquartile range (IQR)112

Descriptive statistics

Standard deviation84.569847
Coefficient of variation (CV)0.77871587
Kurtosis-0.28186028
Mean108.60167
Median Absolute Deviation (MAD)30
Skewness1.0246757
Sum4594611
Variance7152.0589
MonotonicityNot monotonic
2024-01-27T15:11:37.320818image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
82 3649
 
8.6%
83 2863
 
6.8%
84 1546
 
3.7%
57 1325
 
3.1%
81 1300
 
3.1%
58 1295
 
3.1%
59 1161
 
2.7%
56 1046
 
2.5%
240 892
 
2.1%
241 875
 
2.1%
Other values (218) 26355
62.3%
ValueCountFrequency (%)
0 94
0.2%
1 54
 
0.1%
2 75
 
0.2%
3 62
 
0.1%
4 91
 
0.2%
5 140
0.3%
6 145
0.3%
7 158
0.4%
8 188
0.4%
9 232
0.5%
ValueCountFrequency (%)
360 1
 
< 0.1%
325 7
 
< 0.1%
312 11
 
< 0.1%
311 22
 
0.1%
310 19
 
< 0.1%
309 45
 
0.1%
308 69
0.2%
306 72
0.2%
303 122
0.3%
302 130
0.3%

NoEmp
Real number (ℝ)

ZEROS 

Distinct196
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.7043043
Minimum0
Maximum202
Zeros2994
Zeros (%)7.1%
Negative0
Negative (%)0.0%
Memory size330.6 KiB
2024-01-27T15:11:37.601610image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median4
Q312
95-th percentile40
Maximum202
Range202
Interquartile range (IQR)10

Descriptive statistics

Standard deviation17.488022
Coefficient of variation (CV)1.8020892
Kurtosis40.742751
Mean9.7043043
Median Absolute Deviation (MAD)3
Skewness5.4942159
Sum410560
Variance305.83092
MonotonicityNot monotonic
2024-01-27T15:11:37.883398image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3 5563
13.1%
2 5518
13.0%
4 4902
11.6%
1 4433
 
10.5%
5 3146
 
7.4%
0 2994
 
7.1%
6 1691
 
4.0%
15 975
 
2.3%
16 914
 
2.2%
7 901
 
2.1%
Other values (186) 11270
26.6%
ValueCountFrequency (%)
0 2994
7.1%
1 4433
10.5%
2 5518
13.0%
3 5563
13.1%
4 4902
11.6%
5 3146
7.4%
6 1691
 
4.0%
7 901
 
2.1%
8 602
 
1.4%
9 544
 
1.3%
ValueCountFrequency (%)
202 1
 
< 0.1%
198 1
 
< 0.1%
197 2
< 0.1%
195 1
 
< 0.1%
194 2
< 0.1%
193 2
< 0.1%
192 2
< 0.1%
191 3
< 0.1%
189 3
< 0.1%
188 2
< 0.1%

NewExist
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size330.6 KiB
1.0
33405 
2.0
8902 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters126921
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 33405
79.0%
2.0 8902
 
21.0%

Length

2024-01-27T15:11:38.101020image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-27T15:11:38.265147image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
1.0 33405
79.0%
2.0 8902
 
21.0%

Most occurring characters

ValueCountFrequency (%)
. 42307
33.3%
0 42307
33.3%
1 33405
26.3%
2 8902
 
7.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 84614
66.7%
Other Punctuation 42307
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 42307
50.0%
1 33405
39.5%
2 8902
 
10.5%
Other Punctuation
ValueCountFrequency (%)
. 42307
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 126921
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 42307
33.3%
0 42307
33.3%
1 33405
26.3%
2 8902
 
7.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 126921
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 42307
33.3%
0 42307
33.3%
1 33405
26.3%
2 8902
 
7.0%

CreateJob
Real number (ℝ)

ZEROS 

Distinct49
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.1837285
Minimum0
Maximum70
Zeros28889
Zeros (%)68.3%
Negative0
Negative (%)0.0%
Memory size330.6 KiB
2024-01-27T15:11:38.623257image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32
95-th percentile13
Maximum70
Range70
Interquartile range (IQR)2

Descriptive statistics

Standard deviation5.0939801
Coefficient of variation (CV)2.3326985
Kurtosis23.628207
Mean2.1837285
Median Absolute Deviation (MAD)0
Skewness4.0134768
Sum92387
Variance25.948633
MonotonicityNot monotonic
2024-01-27T15:11:38.865189image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
0 28889
68.3%
3 3293
 
7.8%
1 2519
 
6.0%
4 1334
 
3.2%
8 665
 
1.6%
9 579
 
1.4%
2 548
 
1.3%
10 511
 
1.2%
7 484
 
1.1%
11 448
 
1.1%
Other values (39) 3037
 
7.2%
ValueCountFrequency (%)
0 28889
68.3%
1 2519
 
6.0%
2 548
 
1.3%
3 3293
 
7.8%
4 1334
 
3.2%
5 73
 
0.2%
6 250
 
0.6%
7 484
 
1.1%
8 665
 
1.6%
9 579
 
1.4%
ValueCountFrequency (%)
70 1
 
< 0.1%
60 3
 
< 0.1%
57 5
 
< 0.1%
56 5
 
< 0.1%
50 6
 
< 0.1%
48 12
< 0.1%
47 15
< 0.1%
46 19
< 0.1%
45 16
< 0.1%
40 17
< 0.1%

RetainedJob
Real number (ℝ)

ZEROS 

Distinct83
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.4781478
Minimum0
Maximum140
Zeros26056
Zeros (%)61.6%
Negative0
Negative (%)0.0%
Memory size330.6 KiB
2024-01-27T15:11:39.092073image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q33
95-th percentile15
Maximum140
Range140
Interquartile range (IQR)3

Descriptive statistics

Standard deviation8.1136484
Coefficient of variation (CV)2.3327497
Kurtosis40.299963
Mean3.4781478
Median Absolute Deviation (MAD)0
Skewness5.1823038
Sum147150
Variance65.83129
MonotonicityNot monotonic
2024-01-27T15:11:39.323426image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 26056
61.6%
1 3846
 
9.1%
8 1227
 
2.9%
9 1060
 
2.5%
3 1057
 
2.5%
7 986
 
2.3%
10 837
 
2.0%
2 803
 
1.9%
11 795
 
1.9%
12 769
 
1.8%
Other values (73) 4871
 
11.5%
ValueCountFrequency (%)
0 26056
61.6%
1 3846
 
9.1%
2 803
 
1.9%
3 1057
 
2.5%
4 687
 
1.6%
5 329
 
0.8%
6 575
 
1.4%
7 986
 
2.3%
8 1227
 
2.9%
9 1060
 
2.5%
ValueCountFrequency (%)
140 1
 
< 0.1%
136 1
 
< 0.1%
130 1
 
< 0.1%
118 1
 
< 0.1%
102 2
 
< 0.1%
100 4
< 0.1%
95 4
< 0.1%
91 7
< 0.1%
90 6
< 0.1%
87 7
< 0.1%

FranchiseCode
Real number (ℝ)

ZEROS 

Distinct271
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1955.056
Minimum0
Maximum90709
Zeros26392
Zeros (%)62.4%
Negative0
Negative (%)0.0%
Memory size330.6 KiB
2024-01-27T15:11:39.594238image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile1
Maximum90709
Range90709
Interquartile range (IQR)1

Descriptive statistics

Standard deviation10541.389
Coefficient of variation (CV)5.3918602
Kurtosis35.690306
Mean1955.056
Median Absolute Deviation (MAD)0
Skewness5.919311
Sum82712555
Variance1.1112088 × 108
MonotonicityNot monotonic
2024-01-27T15:11:39.895228image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 26392
62.4%
1 14033
33.2%
960 182
 
0.4%
27760 21
 
< 0.1%
74750 20
 
< 0.1%
72590 18
 
< 0.1%
73000 18
 
< 0.1%
73675 15
 
< 0.1%
34850 15
 
< 0.1%
36680 14
 
< 0.1%
Other values (261) 1579
 
3.7%
ValueCountFrequency (%)
0 26392
62.4%
1 14033
33.2%
960 182
 
0.4%
5725 1
 
< 0.1%
6410 1
 
< 0.1%
9120 1
 
< 0.1%
9450 1
 
< 0.1%
10482 1
 
< 0.1%
10494 1
 
< 0.1%
10528 3
 
< 0.1%
ValueCountFrequency (%)
90709 1
 
< 0.1%
89769 2
< 0.1%
89655 1
 
< 0.1%
89640 2
< 0.1%
89352 1
 
< 0.1%
89350 1
 
< 0.1%
88875 2
< 0.1%
88355 1
 
< 0.1%
87350 3
< 0.1%
86720 3
< 0.1%

RevLineCr
Categorical

MISSING 

Distinct4
Distinct (%)< 0.1%
Missing1079
Missing (%)2.6%
Memory size330.6 KiB
N
27618 
Y
7353 
0
5561 
T
 
696

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters41228
Distinct characters4
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowN
2nd row0
3rd rowN
4th rowN
5th rowN

Common Values

ValueCountFrequency (%)
N 27618
65.3%
Y 7353
 
17.4%
0 5561
 
13.1%
T 696
 
1.6%
(Missing) 1079
 
2.6%

Length

2024-01-27T15:11:40.091791image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-27T15:11:40.265166image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
n 27618
67.0%
y 7353
 
17.8%
0 5561
 
13.5%
t 696
 
1.7%

Most occurring characters

ValueCountFrequency (%)
N 27618
67.0%
Y 7353
 
17.8%
0 5561
 
13.5%
T 696
 
1.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 35667
86.5%
Decimal Number 5561
 
13.5%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
N 27618
77.4%
Y 7353
 
20.6%
T 696
 
2.0%
Decimal Number
ValueCountFrequency (%)
0 5561
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 35667
86.5%
Common 5561
 
13.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
N 27618
77.4%
Y 7353
 
20.6%
T 696
 
2.0%
Common
ValueCountFrequency (%)
0 5561
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 41228
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N 27618
67.0%
Y 7353
 
17.8%
0 5561
 
13.5%
T 696
 
1.7%

LowDoc
Categorical

IMBALANCE  MISSING 

Distinct6
Distinct (%)< 0.1%
Missing531
Missing (%)1.3%
Memory size330.6 KiB
N
34313 
Y
5277 
0
 
684
A
 
570
S
 
540

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters41776
Distinct characters6
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowN
2nd rowN
3rd rowN
4th rowN
5th rowN

Common Values

ValueCountFrequency (%)
N 34313
81.1%
Y 5277
 
12.5%
0 684
 
1.6%
A 570
 
1.3%
S 540
 
1.3%
C 392
 
0.9%
(Missing) 531
 
1.3%

Length

2024-01-27T15:11:40.442290image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-27T15:11:40.624926image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
n 34313
82.1%
y 5277
 
12.6%
0 684
 
1.6%
a 570
 
1.4%
s 540
 
1.3%
c 392
 
0.9%

Most occurring characters

ValueCountFrequency (%)
N 34313
82.1%
Y 5277
 
12.6%
0 684
 
1.6%
A 570
 
1.4%
S 540
 
1.3%
C 392
 
0.9%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 41092
98.4%
Decimal Number 684
 
1.6%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
N 34313
83.5%
Y 5277
 
12.8%
A 570
 
1.4%
S 540
 
1.3%
C 392
 
1.0%
Decimal Number
ValueCountFrequency (%)
0 684
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 41092
98.4%
Common 684
 
1.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
N 34313
83.5%
Y 5277
 
12.8%
A 570
 
1.4%
S 540
 
1.3%
C 392
 
1.0%
Common
ValueCountFrequency (%)
0 684
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 41776
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N 34313
82.1%
Y 5277
 
12.6%
0 684
 
1.6%
A 570
 
1.4%
S 540
 
1.3%
C 392
 
0.9%
Distinct916
Distinct (%)2.2%
Missing150
Missing (%)0.4%
Memory size330.6 KiB
Minimum1977-06-14 00:00:00
Maximum2073-12-06 00:00:00
2024-01-27T15:11:40.828736image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-27T15:11:41.045621image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

MIS_Status
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size330.6 KiB
1
37767 
0
4540 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters42307
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 37767
89.3%
0 4540
 
10.7%

Length

2024-01-27T15:11:41.250382image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-27T15:11:41.411503image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
1 37767
89.3%
0 4540
 
10.7%

Most occurring characters

ValueCountFrequency (%)
1 37767
89.3%
0 4540
 
10.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 42307
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 37767
89.3%
0 4540
 
10.7%

Most occurring scripts

ValueCountFrequency (%)
Common 42307
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 37767
89.3%
0 4540
 
10.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 42307
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 37767
89.3%
0 4540
 
10.7%

Sector
Real number (ℝ)

ZEROS 

Distinct24
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.933439
Minimum0
Maximum81
Zeros9798
Zeros (%)23.2%
Negative0
Negative (%)0.0%
Memory size330.6 KiB
2024-01-27T15:11:41.592896image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q122
median33
Q344
95-th percentile72
Maximum81
Range81
Interquartile range (IQR)22

Descriptive statistics

Standard deviation22.291386
Coefficient of variation (CV)0.67686178
Kurtosis-0.89972227
Mean32.933439
Median Absolute Deviation (MAD)11
Skewness-0.11631806
Sum1393315
Variance496.90589
MonotonicityNot monotonic
2024-01-27T15:11:41.823673image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
0 9798
23.2%
42 7337
17.3%
33 5050
11.9%
44 3868
 
9.1%
23 3867
 
9.1%
61 2505
 
5.9%
72 2478
 
5.9%
22 1998
 
4.7%
62 1191
 
2.8%
53 896
 
2.1%
Other values (14) 3319
 
7.8%
ValueCountFrequency (%)
0 9798
23.2%
11 7
 
< 0.1%
21 28
 
0.1%
22 1998
 
4.7%
23 3867
 
9.1%
31 138
 
0.3%
32 865
 
2.0%
33 5050
11.9%
42 7337
17.3%
44 3868
 
9.1%
ValueCountFrequency (%)
81 169
 
0.4%
72 2478
5.9%
71 337
 
0.8%
62 1191
2.8%
61 2505
5.9%
56 672
 
1.6%
55 29
 
0.1%
54 267
 
0.6%
53 896
 
2.1%
52 84
 
0.2%
Distinct3868
Distinct (%)9.1%
Missing0
Missing (%)0.0%
Memory size330.6 KiB
Minimum1977-03-11 00:00:00
Maximum2073-10-17 00:00:00
2024-01-27T15:11:42.185435image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-27T15:11:42.491029image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

ApprovalFY
Real number (ℝ)

Distinct38
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2001.5378
Minimum1974
Maximum2014
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size330.6 KiB
2024-01-27T15:11:42.707538image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum1974
5-th percentile1991
Q11997
median2003
Q32006
95-th percentile2010
Maximum2014
Range40
Interquartile range (IQR)9

Descriptive statistics

Standard deviation5.8605268
Coefficient of variation (CV)0.0029280121
Kurtosis0.16185187
Mean2001.5378
Median Absolute Deviation (MAD)4
Skewness-0.69419236
Sum84679059
Variance34.345775
MonotonicityNot monotonic
2024-01-27T15:11:42.924927image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
2004 4708
 
11.1%
2007 3777
 
8.9%
2006 3314
 
7.8%
2003 3293
 
7.8%
2005 2712
 
6.4%
1995 2552
 
6.0%
2000 2295
 
5.4%
1996 1739
 
4.1%
2002 1726
 
4.1%
2008 1723
 
4.1%
Other values (28) 14468
34.2%
ValueCountFrequency (%)
1974 4
 
< 0.1%
1977 5
 
< 0.1%
1979 21
 
< 0.1%
1980 56
0.1%
1981 13
 
< 0.1%
1982 78
0.2%
1983 80
0.2%
1984 79
0.2%
1985 134
0.3%
1986 77
0.2%
ValueCountFrequency (%)
2014 9
 
< 0.1%
2013 105
 
0.2%
2012 318
 
0.8%
2011 781
 
1.8%
2010 948
 
2.2%
2009 1158
 
2.7%
2008 1723
4.1%
2007 3777
8.9%
2006 3314
7.8%
2005 2712
6.4%

City
Text

Distinct2703
Distinct (%)6.4%
Missing0
Missing (%)0.0%
Memory size330.6 KiB
2024-01-27T15:11:43.314133image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length30
Median length27
Mean length8.8926892
Min length3

Characters and Unicode

Total characters376223
Distinct characters65
Distinct categories9 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique827 ?
Unique (%)2.0%

Sample

1st rowPHOENIX
2nd rowMCALESTER
3rd rowHAWTHORNE
4th rowNASHVILLE
5th rowPOMONA
ValueCountFrequency (%)
city 1492
 
2.7%
san 1325
 
2.4%
houston 1224
 
2.3%
pittsburgh 958
 
1.8%
lake 812
 
1.5%
salt 716
 
1.3%
new 616
 
1.1%
philadelphia 614
 
1.1%
nashville 600
 
1.1%
pomona 584
 
1.1%
Other values (2347) 45399
83.5%
2024-01-27T15:11:43.887275image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 36799
 
9.8%
E 32773
 
8.7%
O 29514
 
7.8%
L 29423
 
7.8%
N 28734
 
7.6%
I 24008
 
6.4%
S 23939
 
6.4%
R 22162
 
5.9%
T 20421
 
5.4%
C 13141
 
3.5%
Other values (55) 115309
30.6%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 353936
94.1%
Space Separator 12034
 
3.2%
Lowercase Letter 9453
 
2.5%
Other Punctuation 370
 
0.1%
Open Punctuation 262
 
0.1%
Close Punctuation 151
 
< 0.1%
Decimal Number 10
 
< 0.1%
Dash Punctuation 6
 
< 0.1%
Modifier Symbol 1
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 36799
 
10.4%
E 32773
 
9.3%
O 29514
 
8.3%
L 29423
 
8.3%
N 28734
 
8.1%
I 24008
 
6.8%
S 23939
 
6.8%
R 22162
 
6.3%
T 20421
 
5.8%
C 13141
 
3.7%
Other values (16) 93022
26.3%
Lowercase Letter
ValueCountFrequency (%)
o 1044
11.0%
e 998
10.6%
a 940
9.9%
n 940
9.9%
l 873
9.2%
r 813
8.6%
i 696
 
7.4%
s 567
 
6.0%
t 499
 
5.3%
u 258
 
2.7%
Other values (16) 1825
19.3%
Decimal Number
ValueCountFrequency (%)
6 2
20.0%
8 2
20.0%
5 2
20.0%
0 2
20.0%
2 2
20.0%
Other Punctuation
ValueCountFrequency (%)
. 271
73.2%
' 59
 
15.9%
, 40
 
10.8%
Space Separator
ValueCountFrequency (%)
12034
100.0%
Open Punctuation
ValueCountFrequency (%)
( 262
100.0%
Close Punctuation
ValueCountFrequency (%)
) 151
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 6
100.0%
Modifier Symbol
ValueCountFrequency (%)
` 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 363389
96.6%
Common 12834
 
3.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 36799
 
10.1%
E 32773
 
9.0%
O 29514
 
8.1%
L 29423
 
8.1%
N 28734
 
7.9%
I 24008
 
6.6%
S 23939
 
6.6%
R 22162
 
6.1%
T 20421
 
5.6%
C 13141
 
3.6%
Other values (42) 102475
28.2%
Common
ValueCountFrequency (%)
12034
93.8%
. 271
 
2.1%
( 262
 
2.0%
) 151
 
1.2%
' 59
 
0.5%
, 40
 
0.3%
- 6
 
< 0.1%
6 2
 
< 0.1%
8 2
 
< 0.1%
5 2
 
< 0.1%
Other values (3) 5
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 376223
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 36799
 
9.8%
E 32773
 
8.7%
O 29514
 
7.8%
L 29423
 
7.8%
N 28734
 
7.6%
I 24008
 
6.4%
S 23939
 
6.4%
R 22162
 
5.9%
T 20421
 
5.4%
C 13141
 
3.5%
Other values (55) 115309
30.6%

State
Text

Distinct51
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size330.6 KiB
2024-01-27T15:11:44.128751image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters84614
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAZ
2nd rowOK
3rd rowNJ
4th rowTN
5th rowCA
ValueCountFrequency (%)
ca 6893
 
16.3%
tx 4095
 
9.7%
ny 2953
 
7.0%
pa 2849
 
6.7%
fl 1920
 
4.5%
oh 1229
 
2.9%
ut 1166
 
2.8%
tn 1147
 
2.7%
wa 1050
 
2.5%
mn 1004
 
2.4%
Other values (41) 18001
42.5%
2024-01-27T15:11:44.554175image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 14780
17.5%
C 8921
10.5%
N 8694
10.3%
T 7296
 
8.6%
M 5576
 
6.6%
I 4601
 
5.4%
O 4135
 
4.9%
X 4095
 
4.8%
Y 3796
 
4.5%
L 3394
 
4.0%
Other values (14) 19326
22.8%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 84614
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 14780
17.5%
C 8921
10.5%
N 8694
10.3%
T 7296
 
8.6%
M 5576
 
6.6%
I 4601
 
5.4%
O 4135
 
4.9%
X 4095
 
4.8%
Y 3796
 
4.5%
L 3394
 
4.0%
Other values (14) 19326
22.8%

Most occurring scripts

ValueCountFrequency (%)
Latin 84614
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 14780
17.5%
C 8921
10.5%
N 8694
10.3%
T 7296
 
8.6%
M 5576
 
6.6%
I 4601
 
5.4%
O 4135
 
4.9%
X 4095
 
4.8%
Y 3796
 
4.5%
L 3394
 
4.0%
Other values (14) 19326
22.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 84614
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 14780
17.5%
C 8921
10.5%
N 8694
10.3%
T 7296
 
8.6%
M 5576
 
6.6%
I 4601
 
5.4%
O 4135
 
4.9%
X 4095
 
4.8%
Y 3796
 
4.5%
L 3394
 
4.0%
Other values (14) 19326
22.8%
Distinct51
Distinct (%)0.1%
Missing11
Missing (%)< 0.1%
Memory size330.6 KiB
2024-01-27T15:11:44.800449image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters84592
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSD
2nd rowOK
3rd rowNJ
4th rowSD
5th rowCA
ValueCountFrequency (%)
ca 6476
15.3%
nc 3320
 
7.8%
il 2944
 
7.0%
oh 2785
 
6.6%
ri 2541
 
6.0%
tx 2457
 
5.8%
sd 2382
 
5.6%
ny 2197
 
5.2%
pa 1307
 
3.1%
ut 1133
 
2.7%
Other values (41) 14754
34.9%
2024-01-27T15:11:45.205856image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 11316
13.4%
C 10852
12.8%
N 8915
10.5%
I 7447
 
8.8%
T 5135
 
6.1%
O 5117
 
6.0%
L 4216
 
5.0%
M 4099
 
4.8%
D 3776
 
4.5%
H 3208
 
3.8%
Other values (14) 20511
24.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 84592
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 11316
13.4%
C 10852
12.8%
N 8915
10.5%
I 7447
 
8.8%
T 5135
 
6.1%
O 5117
 
6.0%
L 4216
 
5.0%
M 4099
 
4.8%
D 3776
 
4.5%
H 3208
 
3.8%
Other values (14) 20511
24.2%

Most occurring scripts

ValueCountFrequency (%)
Latin 84592
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 11316
13.4%
C 10852
12.8%
N 8915
10.5%
I 7447
 
8.8%
T 5135
 
6.1%
O 5117
 
6.0%
L 4216
 
5.0%
M 4099
 
4.8%
D 3776
 
4.5%
H 3208
 
3.8%
Other values (14) 20511
24.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 84592
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 11316
13.4%
C 10852
12.8%
N 8915
10.5%
I 7447
 
8.8%
T 5135
 
6.1%
O 5117
 
6.0%
L 4216
 
5.0%
M 4099
 
4.8%
D 3776
 
4.5%
H 3208
 
3.8%
Other values (14) 20511
24.2%
Distinct2694
Distinct (%)6.4%
Missing0
Missing (%)0.0%
Memory size330.6 KiB
2024-01-27T15:11:45.757771image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length14
Median length12
Mean length11.495237
Min length10

Characters and Unicode

Total characters486329
Distinct characters14
Distinct categories4 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique685 ?
Unique (%)1.6%

Sample

1st row$80,000.00
2nd row$287,000.00
3rd row$31,983.00
4th row$229,000.00
5th row$525,000.00
ValueCountFrequency (%)
100,000.00 2773
 
6.6%
50,000.00 2016
 
4.8%
25,000.00 1433
 
3.4%
5,000.00 1298
 
3.1%
60,000.00 1041
 
2.5%
150,000.00 971
 
2.3%
80,000.00 955
 
2.3%
145,000.00 773
 
1.8%
17,000.00 729
 
1.7%
10,000.00 690
 
1.6%
Other values (2684) 29628
70.0%
2024-01-27T15:11:46.878282image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 217564
44.7%
, 43223
 
8.9%
$ 42307
 
8.7%
. 42307
 
8.7%
42307
 
8.7%
5 20651
 
4.2%
1 18647
 
3.8%
2 13317
 
2.7%
3 9574
 
2.0%
4 8893
 
1.8%
Other values (4) 27539
 
5.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 316185
65.0%
Other Punctuation 85530
 
17.6%
Currency Symbol 42307
 
8.7%
Space Separator 42307
 
8.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 217564
68.8%
5 20651
 
6.5%
1 18647
 
5.9%
2 13317
 
4.2%
3 9574
 
3.0%
4 8893
 
2.8%
7 7929
 
2.5%
6 7457
 
2.4%
8 6532
 
2.1%
9 5621
 
1.8%
Other Punctuation
ValueCountFrequency (%)
, 43223
50.5%
. 42307
49.5%
Currency Symbol
ValueCountFrequency (%)
$ 42307
100.0%
Space Separator
ValueCountFrequency (%)
42307
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 486329
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 217564
44.7%
, 43223
 
8.9%
$ 42307
 
8.7%
. 42307
 
8.7%
42307
 
8.7%
5 20651
 
4.2%
1 18647
 
3.8%
2 13317
 
2.7%
3 9574
 
2.0%
4 8893
 
1.8%
Other values (4) 27539
 
5.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 486329
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 217564
44.7%
, 43223
 
8.9%
$ 42307
 
8.7%
. 42307
 
8.7%
42307
 
8.7%
5 20651
 
4.2%
1 18647
 
3.8%
2 13317
 
2.7%
3 9574
 
2.0%
4 8893
 
1.8%
Other values (4) 27539
 
5.7%

GrAppv
Text

Distinct1425
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Memory size330.6 KiB
2024-01-27T15:11:47.306338image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length14
Median length12
Mean length11.469662
Min length10

Characters and Unicode

Total characters485247
Distinct characters14
Distinct categories4 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique294 ?
Unique (%)0.7%

Sample

1st row$80,000.00
2nd row$287,000.00
3rd row$30,000.00
4th row$229,000.00
5th row$525,000.00
ValueCountFrequency (%)
100,000.00 3257
 
7.7%
50,000.00 2934
 
6.9%
25,000.00 2153
 
5.1%
5,000.00 1426
 
3.4%
10,000.00 1330
 
3.1%
60,000.00 1088
 
2.6%
150,000.00 1051
 
2.5%
80,000.00 988
 
2.3%
15,000.00 905
 
2.1%
20,000.00 902
 
2.1%
Other values (1415) 26273
62.1%
2024-01-27T15:11:47.838885image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 234078
48.2%
, 43234
 
8.9%
$ 42307
 
8.7%
. 42307
 
8.7%
42307
 
8.7%
5 20609
 
4.2%
1 16194
 
3.3%
2 11923
 
2.5%
3 7367
 
1.5%
4 6360
 
1.3%
Other values (4) 18561
 
3.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 315092
64.9%
Other Punctuation 85541
 
17.6%
Currency Symbol 42307
 
8.7%
Space Separator 42307
 
8.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 234078
74.3%
5 20609
 
6.5%
1 16194
 
5.1%
2 11923
 
3.8%
3 7367
 
2.3%
4 6360
 
2.0%
7 5694
 
1.8%
6 5310
 
1.7%
8 4480
 
1.4%
9 3077
 
1.0%
Other Punctuation
ValueCountFrequency (%)
, 43234
50.5%
. 42307
49.5%
Currency Symbol
ValueCountFrequency (%)
$ 42307
100.0%
Space Separator
ValueCountFrequency (%)
42307
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 485247
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 234078
48.2%
, 43234
 
8.9%
$ 42307
 
8.7%
. 42307
 
8.7%
42307
 
8.7%
5 20609
 
4.2%
1 16194
 
3.3%
2 11923
 
2.5%
3 7367
 
1.5%
4 6360
 
1.3%
Other values (4) 18561
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 485247
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 234078
48.2%
, 43234
 
8.9%
$ 42307
 
8.7%
. 42307
 
8.7%
42307
 
8.7%
5 20609
 
4.2%
1 16194
 
3.3%
2 11923
 
2.5%
3 7367
 
1.5%
4 6360
 
1.3%
Other values (4) 18561
 
3.8%
Distinct2005
Distinct (%)4.7%
Missing0
Missing (%)0.0%
Memory size330.6 KiB
2024-01-27T15:11:48.151592image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length14
Median length11
Mean length11.284374
Min length10

Characters and Unicode

Total characters477408
Distinct characters14
Distinct categories4 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique443 ?
Unique (%)1.0%

Sample

1st row$68,000.00
2nd row$229,600.00
3rd row$15,000.00
4th row$229,000.00
5th row$393,750.00
ValueCountFrequency (%)
25,000.00 2382
 
5.6%
12,500.00 1705
 
4.0%
90,000.00 1023
 
2.4%
4,250.00 985
 
2.3%
50,000.00 982
 
2.3%
5,000.00 957
 
2.3%
116,000.00 757
 
1.8%
51,000.00 742
 
1.8%
80,000.00 729
 
1.7%
13,600.00 715
 
1.7%
Other values (1995) 31330
74.1%
2024-01-27T15:11:48.738489image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 192470
40.3%
, 42736
 
9.0%
$ 42307
 
8.9%
. 42307
 
8.9%
42307
 
8.9%
5 28769
 
6.0%
2 18959
 
4.0%
1 18682
 
3.9%
3 9861
 
2.1%
7 9728
 
2.0%
Other values (4) 29282
 
6.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 307751
64.5%
Other Punctuation 85043
 
17.8%
Currency Symbol 42307
 
8.9%
Space Separator 42307
 
8.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 192470
62.5%
5 28769
 
9.3%
2 18959
 
6.2%
1 18682
 
6.1%
3 9861
 
3.2%
7 9728
 
3.2%
6 8048
 
2.6%
4 7649
 
2.5%
8 7052
 
2.3%
9 6533
 
2.1%
Other Punctuation
ValueCountFrequency (%)
, 42736
50.3%
. 42307
49.7%
Currency Symbol
ValueCountFrequency (%)
$ 42307
100.0%
Space Separator
ValueCountFrequency (%)
42307
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 477408
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 192470
40.3%
, 42736
 
9.0%
$ 42307
 
8.9%
. 42307
 
8.9%
42307
 
8.9%
5 28769
 
6.0%
2 18959
 
4.0%
1 18682
 
3.9%
3 9861
 
2.1%
7 9728
 
2.0%
Other values (4) 29282
 
6.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 477408
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 192470
40.3%
, 42736
 
9.0%
$ 42307
 
8.9%
. 42307
 
8.9%
42307
 
8.9%
5 28769
 
6.0%
2 18959
 
4.0%
1 18682
 
3.9%
3 9861
 
2.1%
7 9728
 
2.0%
Other values (4) 29282
 
6.1%

UrbanRural
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size330.6 KiB
0
24037 
1
11759 
2
6511 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters42307
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 24037
56.8%
1 11759
27.8%
2 6511
 
15.4%

Length

2024-01-27T15:11:48.950052image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-27T15:11:49.110029image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0 24037
56.8%
1 11759
27.8%
2 6511
 
15.4%

Most occurring characters

ValueCountFrequency (%)
0 24037
56.8%
1 11759
27.8%
2 6511
 
15.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 42307
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 24037
56.8%
1 11759
27.8%
2 6511
 
15.4%

Most occurring scripts

ValueCountFrequency (%)
Common 42307
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 24037
56.8%
1 11759
27.8%
2 6511
 
15.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 42307
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 24037
56.8%
1 11759
27.8%
2 6511
 
15.4%

Interactions

2024-01-27T15:11:33.833940image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-27T15:11:23.435869image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-27T15:11:24.942122image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-27T15:11:26.430771image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-27T15:11:27.903596image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-27T15:11:29.355743image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-27T15:11:30.709099image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-27T15:11:32.263351image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-27T15:11:33.989322image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-27T15:11:23.586099image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-27T15:11:25.119804image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-27T15:11:26.610885image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-27T15:11:28.060944image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-27T15:11:29.509074image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-27T15:11:30.849057image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-27T15:11:32.581543image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-27T15:11:34.180295image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-27T15:11:23.791277image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-27T15:11:25.300371image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-27T15:11:26.847546image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-27T15:11:28.245282image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-27T15:11:29.682974image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-27T15:11:31.005769image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-27T15:11:32.750724image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-27T15:11:34.389694image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-27T15:11:23.965917image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-27T15:11:25.501440image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-27T15:11:27.022653image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-27T15:11:28.419898image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-27T15:11:29.856434image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-27T15:11:31.221120image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-27T15:11:32.921973image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-27T15:11:34.588490image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-27T15:11:24.131885image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-27T15:11:25.673645image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-27T15:11:27.204532image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-27T15:11:28.595679image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-27T15:11:30.028130image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-27T15:11:31.409017image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-27T15:11:33.099951image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-27T15:11:34.764673image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-27T15:11:24.293508image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-27T15:11:25.850051image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-27T15:11:27.378868image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-27T15:11:28.771019image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-27T15:11:30.204581image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-27T15:11:31.650678image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-27T15:11:33.272307image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-27T15:11:34.926225image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-27T15:11:24.551312image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-27T15:11:26.004020image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-27T15:11:27.536861image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-27T15:11:28.950824image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-27T15:11:30.360338image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-27T15:11:31.910120image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-27T15:11:33.478482image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-27T15:11:35.110605image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-27T15:11:24.765517image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-27T15:11:26.218355image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-27T15:11:27.724861image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-27T15:11:29.170788image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-27T15:11:30.533117image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-27T15:11:32.088614image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-27T15:11:33.648489image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Missing values

2024-01-27T15:11:35.442600image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-27T15:11:35.989063image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

Unnamed: 0TermNoEmpNewExistCreateJobRetainedJobFranchiseCodeRevLineCrLowDocDisbursementDateMIS_StatusSectorApprovalDateApprovalFYCityStateBankStateDisbursementGrossGrAppvSBA_AppvUrbanRural
00163211.0001NN31-Jan-981022-Sep-062006PHOENIXAZSD$80,000.00$80,000.00$68,000.000
118461.04000N31-Oct-9316230-Jun-921992MCALESTEROKOK$287,000.00$287,000.00$229,600.000
22242451.04900NN31-Aug-0114218-Apr-012001HAWTHORNENJNJ$31,983.00$30,000.00$15,000.001
3323741.0000NN31-Aug-071336-Oct-032004NASHVILLETNSD$229,000.00$229,000.00$229,000.000
4418401.0000NN8-Jun-831017-Dec-992000POMONACACA$525,000.00$525,000.00$393,750.000
556071.04100YN1-Apr-1204426-Nov-931994APLINGTONIAIA$69,991.00$70,000.00$35,000.000
663901.015100N8-Nov-111234-Jan-052005DALLASTXCA$50,000.00$50,000.00$25,000.000
778252.0001NC31-Jan-951021-Nov-012002HUDSONNHNH$414,000.00$414,000.00$414,000.000
885762.0000NC31-Jan-9516111-Jan-951995WILLISTONNDND$112,500.00$112,500.00$101,250.000
992511.0001NN30-Apr-071023-Mar-042004MESAAZAZ$50,000.00$50,000.00$25,000.002
Unnamed: 0TermNoEmpNewExistCreateJobRetainedJobFranchiseCodeRevLineCrLowDocDisbursementDateMIS_StatusSectorApprovalDateApprovalFYCityStateBankStateDisbursementGrossGrAppvSBA_AppvUrbanRural
42297422975721.000960NN29-Jun-01108-Mar-961996SACRAMENTOCACO$105,000.00$105,000.00$89,250.000
422984229882151.0110725900N30-Apr-0614415-May-032003DALLASTXTX$50,000.00$50,000.00$25,000.000
42299422991021.0010YN30-Jun-080728-Mar-002000CLARENCENYNY$670,000.00$670,000.00$670,000.001
42300423008231.0001NN31-Dec-051429-Jul-032003NEW YORKNYIL$150,000.00$150,000.00$135,000.000
42301423018201.01811YN10-Dec-961237-Jul-092009PITTSBURGHPAPA$5,000.00$5,000.00$4,250.001
4230242302283141.0001NN31-Jan-98102-Mar-951995PHILADELPHIAPAPA$80,000.00$80,000.00$68,000.000
42303423035321.0000YN3-Apr-911426-Jun-072007LOS ANGELESCASD$5,000.00$5,000.00$4,250.001
42304423045962.0001NN28-Feb-0314214-Mar-032003COLUMBUSOHOH$60,000.00$60,000.00$51,000.000
4230542305295181.0080NN10-Dec-9714223-Aug-891989CLOQUETMNMN$294,000.00$294,000.00$220,500.000
42306423068441.0080NN31-Oct-8917212-Apr-112011SAN GABRIELCANC$67,500.00$67,500.00$50,625.000